Coordination Mechanism for Network Automation Conflict Resolution
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Solution Overview
Problem
Cognitive Autonomous Networks (CAN) face challenges in adapting to rapidly changing environments and maintaining system integrity due to the inability of Self Organizing Network (SON) functions to adapt and the complexity of rule-based coordination.
Innovation Solution
The implementation of a coordination and control mechanism that dynamically resolves conflicts among Cognitive Functions (CFs) by receiving configuration sets from multiple network automation functions, determining optimal network configurations, and using utility functions to achieve the best performance targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based SON coordination is used to manage SON functions, then conflict resolution among functions is achieved, but the system cannot adapt in rapidly changing environments and maintenance becomes difficult
Solution Approach 1:
The patent transitions from static rule-based coordination to dynamic cognitive functions that can adapt their behavior based on changing network conditions. The cognitive functions learn from environmental changes and adjust their coordination strategies in real-time, enabling the system to respond flexibly to rapidly changing environments while maintaining conflict resolution capabilities.
Solution Approach 2:
The invention changes the fundamental parameters of the coordination system by replacing fixed rules with learning-based cognitive functions. These cognitive functions can modify their internal parameters and decision-making processes based on observed patterns and environmental feedback, enabling adaptation to new scenarios without requiring manual rule updates.
2Productivity
If multiple SON functions are deployed to manage specific targets, then network automation capability is improved, but conflicts arise among functions making coordination difficult
Solution Approach 1:
The patent introduces cognitive functions as intermediary entities between the network infrastructure and traditional SON functions. These cognitive intermediaries learn the behavior patterns of multiple SON functions and mediate their interactions, resolving conflicts through learned strategies rather than complex rule-based coordination, thereby simplifying the overall system complexity.
Solution Approach 2:
The cognitive functions employ self-service mechanisms by autonomously learning coordination strategies and conflict resolution patterns without requiring external intervention. The system automatically adapts to new conflict scenarios through continuous learning, eliminating the need for manual coordination rule maintenance and reducing operational complexity.
3Adaptability or versatility
If cognitive functions replace SON functions, then adaptability to changing environments is improved, but conflict resolution mechanisms become more complex
Solution Approach 1:
The patent replaces the mechanical rule-based coordination system with a learning-based cognitive system. Instead of relying on predefined mechanical rules for conflict resolution, the cognitive functions use learned models and patterns to dynamically resolve conflicts, reducing the apparent complexity while maintaining adaptability to changing environments.
Data Source
AI summary
An apparatus and a method are provided, by which a controller receives configuration sets from at least two network automation functions, each network automation function determining the configuration set such that it is optimal for achieving an objective of the network automation function. The controller determines an optimal network configuration based on the configuration sets received from the at least two network automation functions.